The Science Data Understanding Group carries out research and technology development in computational techniques to extract knowledge from science data, and distribute it to science users.

We believe that new computational capabilities, from detectors to flight and ground computers and through to the Internet, are revolutionizing the way science phenomena are captured as digital data, the way knowledge is extracted from these data, and the way data and knowledge are exchanged by the scientific community.

What we do: Model the behavior and statistical variability of physical systems like oceans and atmospheres, recognize patterns, infer parameters, and quantify uncertainty in massive data sets taken from such systems, and distribute the resulting data in a web context.

Our work covers these general topic areas:

Uncertainty Quantification, Data Fusion, and Massive Dataset Analysis

  • Uncertainty quantification (UQ) for atmospheric retrievals from OCO-2, MLS, ECOSTRESS, and MAIA
  • Uncertainty assessment and propagation for a large-scale hydrological routing model, and uncertainty assessment for groundwater using models and gravimetric observations
  • Spatial and spatio-temporal data fusion for atmospheric fields in a Bayesian context, with applications to near surface temperature products from AIRS
  • Developed a leading system for recognizing, grouping, and tracking solar active regions used for SOHO and distributed as a data product for SDO
  • Developed multi-observation compressed summaries for MISR and AIRS, which have been distributed as a Level 3 data product
  • Proprioceptive and appearance-based terrain classification for autonomous rover navigation (DARPA, ARO)

Physical Modeling and Inversion

  • Inversion of cosmic microwave background to recover spherical harmonic power, and structural parameters of cosmological models (Wilkinson Microwave Anisotropy Probe and Planck)
  • Developing new statistical methods for assessing agreement between climate model simulations and observations in a distributed data context
  • Data assimilation and Bayesian risk assessment for highly nonlinear weather systems

Web Services, Virtual Observatories, Mapping, GIS

  • GIS for Mars landing site selection and Mars surface operations
  • Oceanographic data portals for salinity, sea surface temperature, hurricane study, and ocean field campaigns.

A 2019 seminar highlighted recent activities in data fusion and uncertainty quantification:

Earlier seminars: